Bibliographic record
Abstract
Abstract For successful delivery of Well Integrity, there needs to be an understanding of the risks that can cause undesirable events such as safety hazards or loss of containment. Performing a risk assessment on a well, or type of well, will help determine and rank the potential risks and provide information that allows limited resources to be applied in the most effective manner. The main objectives of performing a risk assessment include: Follow a formal process to assess risk consistently and to enable comparison between well barrier failure mode scenarios;Qualitatively assess well barrier failure risk for every segment of a well;Document suggestions that are offered by the risk assessment team for mitigating well barrier failure risk; andProvide a report of the methodology, failure mode scenarios, risk ranking, and potential mitigation actions for use as a reference tool for managing well integrity on a routine basis. The Well Integrity Risk Assessment Model follows a common qualitative risk assessment process; a team-based, structured brainstorming format, utilizing the What-If Methodology, to identify potential hazards associated with well barrier failure modes. In addition, the model has the following attributes: Incorporates a unique method to segment well barriers into discrete sections, successively "failing" each section for evaluation. The list of analyzed well barrier failure modes, along with their risk ranking, becomes the risk register for the well or type of well.Is adaptable for assessing well barrier failure modes on a single well, or a group of wells, having the same general design parameters. An entire well portfolio can be assessed quickly by analyzing types of wells rather than individual wells.Can be used to assess well barrier failure risk for any type of well.The model can easily be modified to conform to any company's risk model. The Well Integrity Risk Assessment Model has been proven to: Successfully assess well barrier failure risk for thousands of wells;Focus specifically on well barrier failure modes, and as a result is an effective tool that should be incorporated into a "Best in Class" Well Integrity Program;Be utilized as a management tool to provide guidance for how limited resources can be used effectively to continuously deliver well integrity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".